Flexible and Efficient Topological Approaches for a Reliable Robots Swarm Aggregation

Belkacem Khaldi, Fouzi Harrou, Foudil Cherif, Ying Sun

Research output: Contribution to journalArticlepeer-review

4 Scopus citations

Abstract

Aggregation is a vital behavior when performing complex tasks in most of the swarm systems such as swarm robotics systems. In this paper, three new aggregation methods, namely the Distance-Angular, the Distance-Cosine, and the Distance-Minkowski k-nearest neighbor (k-NN) have been introduced. These aggregation methods are mainly built on well-known metrics: the Cosine, Angular and Minkowski distance functions, which are used here to compute distances among robots neighbors. Relying on these methods, each robot identifies its k nearest neighborhood set that will interact with. Then in order to achieve the aggregation, the interactions sensing capabilities among the set members are modeled using a virtual viscoelastic mesh. Analysis of the results obtained from the ARGoS simulator shows a significant improvement in the swarm aggregation performance while compared to the conventional distance-weighted k-NN aggregation method. Also, the aggregation performance of the methods is reported to be robust to partially faulty robots and accurate under noisy sensors.
Original languageEnglish (US)
Pages (from-to)96372-96383
Number of pages12
JournalIEEE Access
Volume7
DOIs
StatePublished - Jul 23 2019

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